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Full Description
This book explores the critical issue of fairness in human-machine interfaces. It delves into the integration of technology and cognitive science to develop AI systems that are unbiased, reliable, and user-friendly. The book also sheds light on emotional data processing in AI accelerators and federated learning modules. Additionally, it covers machine learning, knowledge representation, and the application of knowledge graphs to understand and optimize the behaviour of AI assistance devices.
Features:
Explains complex issues of Cognitive Fairness Aware Contextual Proactive Federated Protocol collects data and identifies individual emotional issues and resolves them by contextual solitary proactive communication
Discusses emotional data processing challenges through AI accelerator with federated learning module to generate periodical counselling messages
Addresses data analysis anomalies in Graph Database Modelling by anom-aly prediction and anomaly detection
Describes anomaly detection techniques in the form of abnormal data records, messages, events, groups, and/or other unexpected observations in graph database modelling
Explains how outlier detection for data analysis deals with the detection of patterns in Graph Database
This book is for researchers, academics, students, AI practitioners and developers, ethics experts in AI technology and machine-learning practitioners interested in fairness in human-machine interfaces.
Contents
1. Federated learning by Contextual Model for Advanced AI Assistance 2. Computational Modeling for Personalized Emotion Data and Visual Analytics to Predicting Habits 3. A review on Computational modeling for Personalize demotion and visual analytics to predicting habits 4. An impact of AI-Driven Sentiment Analysis Improves Stock Market Trend Predictions, Risk Management, and Ethics 5. Transformative Strategies for AIED Interaction on the evolution of AI Learning Companions in the Era of Human-Robot Interaction in EFL Settings 6. Comprehensive Overview of Graph Database: Types, Algorithms, Visualization Tools, Applications, and Key Challenges 7. Context-aware Knowledge Base Engineering for Anomaly Detection and Predictive Maintenance in Graph Databases 8. Context Anomaly Identification Algorithm using Dirichlet Graph based mapping in health care analytics 9. Human-Machine Interaction Failure for Indian Companies-An Exploratory Study 10. Practical Solutions for Data Consistency and Query Performance in Graph Database and Search Engine Integration 11. Co-evolution of Human and Machine Intelligence 12. A Novel Graph Machine Learning Pipeline for Anomaly Detection 13. Implementing a Graph Machine Learning Pipeline for Anomaly Detection 14. Proactive Human - Machine Collaboration 15. Proactive Assistance between Human and Machine
